619 citations · 6.4k across the 178 of their papers we have counts for
13 papers · 1 filter
Deep Learning with S-shaped Rectified Linear Activation Units
Xiaojie Jin, Chunyan Xu, Jiashi Feng +3
Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation…
Return of Frustratingly Easy Domain Adaptation
Baochen Sun, Jiashi Feng, Kate Saenko
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scena…
Reversible Recursive Instance-level Object Segmentation
Xiaodan Liang, Yunchao Wei, Xiaohui Shen +4
In this work, we propose a novel Reversible Recursive Instance-level Object Segmentation (R2-IOS) framework to address the challenging instance-level object segmentation task. R2-I…
Learning with -Graph: -Induced Sparse Subspace Clustering
Yingzhen Yang, Jiashi Feng, Jianchao Yang +1
Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC) \cite{ElhamifarV13} and -graph \cite{YanW09,ChengYYFH10}, are effective in partitioning the d…
Semantic Object Parsing with Local-Global Long Short-Term Memory
Xiaodan Liang, Xiaohui Shen, Donglai Xiang +3
Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical f…
Auxiliary Image Regularization for Deep CNNs with Noisy Labels
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka +1
Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world d…
Natural Language Object Retrieval
Ronghang Hu, Huazhe Xu, Marcus Rohrbach +3
In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object. Natural l…
Deep Recurrent Regression for Facial Landmark Detection
Hanjiang Lai, Shengtao Xiao, Yan Pan +5
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent netw…
Scale-aware Fast R-CNN for Pedestrian Detection
Jianan Li, Xiaodan Liang, ShengMei Shen +3
In this work, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically differ…
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
Yunchao Wei, Xiaodan Liang, Yunpeng Chen +5
Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually r…
Sense Beyond Expressions: Cuteness
Kang Wang, Tam V. Nguyen, Jiashi Feng +1
With the development of Internet culture, cuteness has become a popular concept. Many people are curious about what factors making a person look cute. However, there is rare resear…
Modality-dependent Cross-media Retrieval
Yunchao Wei, Yao Zhao, Zhenfeng Zhu +4
In this paper, we investigate the cross-media retrieval between images and text, i.e., using image to search text (I2T) and using text to search images (T2I). Existing cross-media…
Correlation Adaptive Subspace Segmentation by Trace Lasso
Canyi Lu, Jiashi Feng, Zhouchen Lin +1
This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they…